Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks
(2008) Abstract
 This thesis is based on 5 papers exploring the filtering problem in nonlinear nonGaussian statespace models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks.
The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength measurements. Two different approaches for mobile movement(polar and Cartesian)were used, combined with two different models for the received signal strength. The results of the simulation study showed better performance for particle filters based on a power model with varying propagation coefficient. The filters based on the polar model for mobile... (More)  This thesis is based on 5 papers exploring the filtering problem in nonlinear nonGaussian statespace models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks.
The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength measurements. Two different approaches for mobile movement(polar and Cartesian)were used, combined with two different models for the received signal strength. The results of the simulation study showed better performance for particle filters based on a power model with varying propagation coefficient. The filters based on the polar model for mobile movement were found to be more precise in terms of mean squared error, but at the same time were more computationally intensive.
The second paper represents the results of a simulation study on mobile positioning in multiply input multiply output (MIMO) settings. Three different particles filters were implemented for the positioning, and simulation results showed that all filters were able to achieve estimation accuracy required by Federal Communication Commission (FCC). Moreover, since dimensionality of the particle filter state space does not depend on the antenna configuration, it is possible to apply described filters in more sophisticated MIMO setup without changing the algorithms.
In the third paper we investigated an algorithm for particles filtering in multidimensional statespace models which are decomposable in the states. We demonstrated using the simulations that the algorithm effectively reduces the computational time without a large precision loss.
It is known that the quality of sequential Monte Carlo estimation depends on the number of particles involved. In the paper four we explored different strategies to increase the number of particles: correlated sampling and observationdriven sampling. The correlated sampling approach was further investigated in the fifth paper, where we employed the idea of using antithetic variates. We introduced a version of the standard auxiliary particle filter and concluded, based on the theoretical developments, that the asymptotic variance of the produced Monte Carlo estimates can be decreased by means of antithetic techniques when the particle filter is closed to fully adapted, which involves approximation of the socalled optimal proposal kernel. As an illustration, the method was applied to optimal filtering in statespace models. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/1227795
 author
 Bizjajeva, Svetlana ^{LU}
 supervisor

 Tobias Rydén ^{LU}
 opponent

 Gustafsson, Frederik, Linköpings Universitet, Linköping
 organization
 publishing date
 2008
 type
 Thesis
 publication status
 published
 subject
 keywords
 positioning, statespace models, SMCM, particle filtering
 pages
 153 pages
 publisher
 Department of Mathematical Statistics, Lund University
 defense location
 Room MH:A, Matematikcentrum, Sölvegatan 18, Lund
 defense date
 20081003 13:15
 ISSN
 14040034
 ISBN
 9789162875732
 language
 English
 LU publication?
 yes
 id
 34c13bb4265948018ee78a34018dcfdf (old id 1227795)
 alternative location
 http://www.maths.lth.se/matstat/staff/svetik/thesisSB.pdf
 date added to LUP
 20080905 13:50:58
 date last changed
 20160919 08:44:46
@phdthesis{34c13bb4265948018ee78a34018dcfdf, abstract = {This thesis is based on 5 papers exploring the filtering problem in nonlinear nonGaussian statespace models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks. <br/><br> <br/><br> The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength measurements. Two different approaches for mobile movement(polar and Cartesian)were used, combined with two different models for the received signal strength. The results of the simulation study showed better performance for particle filters based on a power model with varying propagation coefficient. The filters based on the polar model for mobile movement were found to be more precise in terms of mean squared error, but at the same time were more computationally intensive.<br/><br> <br/><br> The second paper represents the results of a simulation study on mobile positioning in multiply input multiply output (MIMO) settings. Three different particles filters were implemented for the positioning, and simulation results showed that all filters were able to achieve estimation accuracy required by Federal Communication Commission (FCC). Moreover, since dimensionality of the particle filter state space does not depend on the antenna configuration, it is possible to apply described filters in more sophisticated MIMO setup without changing the algorithms.<br/><br> <br/><br> In the third paper we investigated an algorithm for particles filtering in multidimensional statespace models which are decomposable in the states. We demonstrated using the simulations that the algorithm effectively reduces the computational time without a large precision loss. <br/><br> <br/><br> It is known that the quality of sequential Monte Carlo estimation depends on the number of particles involved. In the paper four we explored different strategies to increase the number of particles: correlated sampling and observationdriven sampling. The correlated sampling approach was further investigated in the fifth paper, where we employed the idea of using antithetic variates. We introduced a version of the standard auxiliary particle filter and concluded, based on the theoretical developments, that the asymptotic variance of the produced Monte Carlo estimates can be decreased by means of antithetic techniques when the particle filter is closed to fully adapted, which involves approximation of the socalled optimal proposal kernel. As an illustration, the method was applied to optimal filtering in statespace models.}, author = {Bizjajeva, Svetlana}, isbn = {9789162875732}, issn = {14040034}, keyword = {positioning,statespace models,SMCM,particle filtering}, language = {eng}, pages = {153}, publisher = {Department of Mathematical Statistics, Lund University}, school = {Lund University}, title = {Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks}, year = {2008}, }